Covid Report for Family

Background

Notes and format last updated May 7, 2020

Starting on the May 7th update, the NY Times began including probable covid cases/deaths along with confirmed. This mostly affects death counts – for certain geographies that include probable COVID deaths in addition to confirmed, these are now added to the totals. For the time being, they were all added to the May 6th totals, causing a big spike at the U.S. level. Over time, NY Times will revise their historical counts and distribute these added deaths when they actually occurred, so the spike should fade.

• This page shows COVID data broken down into regions that affect our family. It pulls from publicly available raw data from the NY Times, which is reported daily. It’s on a one-day lag, so when you see this report, the most recent day included will be that reported the day prior.
• The table of contents above summarizes the major sections and can be used to jump to those sections.
• Each major section is then divided into tabs to allow you to more easily shuffle between geographies and metrics.
  • If you open on your phone, I don’t believe the tabs will work, so you’ll just see the page as one long, continuous document.
• More detailed descriptions and context can be found in each section.

Growth rates

Heat maps

• The two heat maps below compare how quickly total cases or deaths have grown at various times in our respective geopgraphies.
• The first plot compares growth rate for total cases; the second, growth rate for total deaths.
• The metric used is doubling time, by which I mean how quickly total cases or deaths are doubling.
• The plots track that doubling time at each date for our geographies. Darker colors reflect shorter doubling times, and thus periods of faster growth.
  • You can use the plots to track each geography over time and to compare the geographies to one another.
  • You can also compare the cases and death charts, to see how faster periods of death growth follow faster periods of case growth.

Case growth rates

• This section charts the growth rate of both total and new cases for each of our respective geographies. Each geography has its own chart, and then that chart will have a trendline for total cases and new cases.
  • There are only plots for the U.S. and states because the numbers for the counties are too small to generate worthwhile trendlines in this section.
• Note that we’re charting growth rate and not a count of cases, so don’t think of these as the standard “curve” that we hear about in the news and that we want to flatten. Instead, these growth rate charts help track more precisely what we can only estimate when we see those other curves. For these growth rate charts, if the line is above zero, the metric we are tracking (total or new cases) is continuing to grow. If the growth rate line is going up, it’s growing more quickly each day; if it’s going down but still above zero, it’s growing less quickly (but still growing). Only when the growth rate lines go below zero has the metric stopped growing.
• Each of these two lines uses rolling windows to calculate a growth rate for that particular metric. I do the calculation differently for each to smooth out some of the large day-to-day discrepancies in new case reporting at the state level.
  • For total cases, the trendlines are a rolling 3-day average of daily growth rates in total cases. We want to see these decline (and almost all are), but they can’t go below zero. This is because we’re tracking growth rate and a growth rate line below zero would mean total cases have gone down, which can’t happen. They can only grow less quickly, which means we want to see the total case line get as close to zero as possible.
  • For new cases, the trendlines show a rolling 3-day average of daily growth rate in the rolling 7-day average of new cases. Including two rolling periods in this average helps smooth out crazy spikes at the state level that result from large day-to-day changes. Unlike the lines for total cases, we want to watch for the lines for new cases to get consistently below zero and stay there. That means that we are consistently seeing fewer new cases on a daily basis.

U.S.

Our states

Death growth rates

• This section charts the growth rate of both total and new deaths for each of our respective geographies. Each geography has its own chart, and then that chart will have a trendline for total deaths and new deaths.
  • There are only plots for the U.S. and states because the numbers for the counties are too small to generate worthwhile trendlines in this section.
• Note that we’re charting growth rate and not a count of deaths, so don’t think of these as the standard “curve” that we hear about in the news and that we want to flatten. Instead, these growth rate charts help track more precisely what we can only estimate when we see those other curves. For these growth rate charts, if the line is above zero, the metric we are tracking (total or new deaths) is continuing to grow. If the growth rate line is going up, it’s growing more quickly each day; if it’s going down but still above zero, it’s growing less quickly (but still growing). Only when the growth rate lines go below zero has the metric stopped growing.
• Each of these two lines uses rolling windows to calculate a growth rate for that particular metric. I do the calculation differently for each to smooth out some of the large day-to-day discrepancies in new death reporting at the state level.
  • For total deaths, the trendlines are a rolling 3-day average of daily growth rates in total deaths. We want to see these decline (and almost all are), but they can’t go below zero. This is because we’re tracking growth rate and a growth rate line below zero would mean total deaths have gone down, which can’t happen. They can only grow less quickly, which means we want to see the total death line get as close to zero as possible.
  • For new deaths, the trendlines show a rolling 3-day average of daily growth rate in the rolling 7-day average of new deaths. Including two rolling periods in this average helps smooth out crazy spikes at the state level that result from large day-to-day changes. Unlike the lines for total deaths, we want to watch for the lines for new deaths to get consistently below zero and stay there. That means that we are consistently seeing fewer new deaths on a daily basis.

U.S.

Our states

By population rankings

This section tracks metrics for states and counties normalized for population (number of cases or deaths per million residents), and then compares these figures both for our geographies and the country overall.

States

• This section shows tables ranking all 50 states for per populations rates of total cases, new cases, total deaths, and new deaths.
• For each metric, in addition to the tables, the trends for the top states are plotted over time.
  • We only plot the top ten states for each metric so that the plots aren’t too crowded. But you can view the full 50-state rankings in the tables.

Total confirmed cases

Table of total confirmed cases per million residents (all 50 states)

Ranking	State	Cases Per Million
1	Louisiana	23,164
2	Arizona	22,262
3	New York	21,407
4	New Jersey	20,409
5	Florida	19,734
6	Mississippi	17,574
7	Rhode Island	17,202
8	Massachusetts	16,777
9	District of Columbia	16,691
10	Alabama	16,171
11	South Carolina	15,770
12	Delaware	14,675
13	Georgia	14,455
14	Maryland	13,944
15	Nevada	13,934
16	Connecticut	13,680
17	Illinois	13,658
18	Texas	13,648
19	Iowa	13,425
20	Tennessee	13,373
21	Arkansas	12,798
22	Nebraska	12,726
23	Utah	11,844
24	California	11,625
25	North Carolina	10,762
26	Idaho	10,218
27	Virginia	9,907
28	South Dakota	9,489
29	Indiana	9,488
30	New Mexico	9,086
31	Minnesota	9,077
32	Wisconsin	9,048
33	Kansas	8,804
34	Pennsylvania	8,730
35	Michigan	8,686
36	Oklahoma	7,906
37	North Dakota	7,715
38	Colorado	7,703
39	Ohio	7,192
40	Washington	7,146
41	Missouri	7,083
42	Kentucky	6,196
43	Puerto Rico	4,741
44	New Hampshire	4,733
45	Wyoming	4,276
46	Alaska	4,237
47	Oregon	3,974
48	West Virginia	3,325
49	Montana	3,126
50	Maine	2,837
51	Vermont	2,243
52	Hawaii	1,172

New confirmed cases

Table of new cases per million residents: rolling 3-day average (all 50 states)

Ranking	State	New Cases Per Million
1	Florida	527
2	Mississippi	476
3	Louisiana	424
4	Arizona	412
5	Alabama	345
6	Tennessee	325
7	Nevada	306
8	Georgia	301
9	South Carolina	297
10	Oklahoma	279
11	Arkansas	261
12	Idaho	261
13	Texas	225
14	California	220
15	Missouri	193
16	Alaska	190
17	Utah	186
18	North Carolina	182
19	Puerto Rico	174
20	North Dakota	167
21	Wisconsin	167
22	Iowa	164
23	Maryland	160
24	Minnesota	143
25	New Mexico	141
26	Indiana	138
27	Kentucky	138
28	Nebraska	137
29	Montana	130
30	Virginia	130
31	Kansas	129
32	Delaware	125
33	Colorado	113
34	Illinois	113
35	Ohio	110
36	District of Columbia	98
37	South Dakota	94
38	Washington	94
39	Oregon	78
40	West Virginia	76
41	Wyoming	73
42	Michigan	72
43	Pennsylvania	68
44	New Jersey	55
45	Connecticut	50
46	Massachusetts	47
47	Hawaii	45
48	New York	34
49	New Hampshire	28
50	Rhode Island	23
51	Maine	19
52	Vermont	12

Total deaths

Table of total deaths per million residents (all 50 states)

Ranking	State	Deaths Per Million
1	New Jersey	1,777
2	New York	1,660
3	Connecticut	1,237
4	Massachusetts	1,237
5	Rhode Island	945
6	District of Columbia	823
7	Louisiana	809
8	Michigan	641
9	Illinois	599
10	Delaware	594
11	Maryland	569
12	Pennsylvania	560
13	Mississippi	502
14	Arizona	455
15	Indiana	431
16	Georgia	322
17	Colorado	311
18	Alabama	300
19	New Hampshire	300
20	New Mexico	292
21	South Carolina	289
22	Minnesota	286
23	Ohio	282
24	Florida	272
25	Iowa	262
26	Virginia	243
27	Nevada	238
28	California	213
29	Washington	209
30	Missouri	200
31	Texas	175
32	North Carolina	172
33	Nebraska	166
34	Kentucky	160
35	Wisconsin	154
36	South Dakota	139
37	Tennessee	139
38	North Dakota	135
39	Arkansas	132
40	Oklahoma	125
41	Kansas	114
42	Vermont	89
43	Maine	88
44	Utah	86
45	Idaho	83
46	Oregon	68
47	Puerto Rico	62
48	West Virginia	57
49	Montana	43
50	Wyoming	43
51	Alaska	24
52	Hawaii	17

New deaths

Table of new deaths per million residents: rolling 3-day average (all 50 states)

Ranking	State	New Deaths Per Million
1	Delaware	17
2	Arizona	11
3	South Carolina	10
4	Mississippi	6
5	Alabama	5
6	Florida	5
7	Louisiana	5
8	Georgia	4
9	Texas	4
10	California	2
11	Massachusetts	2
12	Nevada	2
13	New Jersey	2
14	New Mexico	2
15	Tennessee	2
16	Washington	2
17	Arkansas	1
18	Idaho	1
19	Indiana	1
20	Kansas	1
21	Maryland	1
22	Missouri	1
23	North Carolina	1
24	Ohio	1
25	Oklahoma	1
26	Oregon	1
27	Puerto Rico	1
28	Alaska	0
29	Colorado	0
30	Connecticut	0
31	District of Columbia	0
32	Hawaii	0
33	Illinois	0
34	Iowa	0
35	Kentucky	0
36	Maine	0
37	Michigan	0
38	Minnesota	0
39	Montana	0
40	Nebraska	0
41	New Hampshire	0
42	New York	0
43	North Dakota	0
44	Pennsylvania	0
45	Rhode Island	0
46	South Dakota	0
47	Utah	0
48	Vermont	0
49	Virginia	0
50	West Virginia	0
51	Wisconsin	0
52	Wyoming	0

Counties

• This section focuses on the county level. It shows tables with our counties ranked by percentile of U.S. counties for per population rates of total cases and total deaths.
  • Each table also shows the top five counties in the country in addition to our counties, for added perspecive.
• In addition to the tables, our counties’ percentile for both total cases and total deaths are plotted over time.

Confirmed cases

Table showing total cases per million and percentile for all US counties. Includes our counties and the top 5 in the US for perspective.

County	State	Cases Per Million	Raw Ranking	Percentile
Trousdale	Tennessee	138,160	1	99
Lake	Tennessee	101,197	2	99
Lee	Arkansas	98,905	3	99
Dakota	Nebraska	94,228	4	99
Buena Vista	Iowa	90,469	5	99
Davidson	Tennessee	28,444	114	96
Richland	South Carolina	16,854	384	87
Orange	California	10,824	810	74
York	South Carolina	10,022	882	71
Pierce	Washington	5,293	1609	48

Our county percentiles over time

Deaths

Table showing total deaths per million and percentile for all US counties. Includes our counties and the top 5 in the US for perspective.

County	State	Deaths Per Million	Raw Ranking	Percentile
Hancock	Georgia	4,020	1	99
Randolph	Georgia	3,836	2	99
Terrell	Georgia	3,399	3	99
Early	Georgia	3,042	4	99
McKinley	New Mexico	2,999	5	99
Richland	South Carolina	305	628	80
Davidson	Tennessee	268	703	77
Orange	California	178	964	69
Pierce	Washington	146	1090	65
York	South Carolina	85	1451	53

Our county percentiles over time

Raw counts

• This section shows the raw counts over time for the U.S., our states, and our counties for four categories:
  • Total confirmed cases: This is the cumulative total of cases reported.
  • New confirmed cases: This is the number of new cases reported on each respective date.
    • For the U.S. and states, a trendline shows the rolling 7-day average of new cases, to help better visualize the peak.
  • Total deaths: This is the cumulative total of deaths reported.
  • New deaths: This is the number of new deaths reported on each respective date.
    • For the U.S. and states, a trendline shows the rolling 7-day average of new deaths, to help better visualize the peak.

Total confirmed cases

U.S.

Our states

Our counties

New confirmed cases

U.S.

Our states

Our counties

Total deaths

U.S.

Our states

Our counties

New deaths

U.S.

Our states

Our counties

Stay-at-home comparisons

• This section is a work in progress. It’s exploring what effect stay-at-home orders have on spread. It’s inherentely messy because some states have localized orders but not orders for their full state and because states implemented the orders at different times.
  • The first plot shows a rolling 3-day average of new cases per million residents, averaged across 2 groups of states – those with a state-wide stay-at-home order in effect and those without. The size of the dots reflect the number of states in each group for each given day (so the dots are getting bigger over time for the group with orders, as more states implement those orders).
    • So far, this view doesn’t show if these orders are “working” or not. Instead, the biggest takeaway from the current plot is that the states that implemented orders are reacting to having worse spread, which is why the case rate is so much higher at the start for them. As a state decides it has a problem and implements an order, it joins the grouping of states with orders and drives the rate up for that group.
    • Over time (if the policies work), what we’d expect is that the line for the group with orders flattens or declines in relation to the no-order group. It also may become moot if all states issue orders.
    • As states start rescending their orders, I’ll put them back into the no-order group, so the no-order group should beging to expand again.
  • The second plot tracks progress of states by the number of days since orders have been in effect. It includes only those states with orders and shows their average new case growth rate (a rolling 3-day average of daily growth rates) based on how long it’s been since the order went into effect. Again, the size of the dots show how many states are included in each point – i.e., as you go further right, the dots are smaller because less states are that far past their order.
    • I’m working on how to add a baseline to this chart to compare to states without orders, but that’s challenging because there’s no way to fully align timing.